
## All my imports
print("Versions")
# Data science
import pandas as pd
print(f"Pandas: {pd.__version__}")
import numpy as np
print(f"Numpy: {np.__version__}")
# Visualization 
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style="ticks", color_codes=True)
import os
# Text Stuff
from sklearn.feature_extraction.text import CountVectorizer
import nltk
from nltk.corpus import stopwords
import sys
sys.path.insert(1, '../')
import research
import re
# ML
from sklearn.ensemble import RandomForestClassifier
from sklearn.pipeline import Pipeline
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV

plt.style.use("ggplot")

# Creating an object where my NLP EDA functions are located
nlp = research.nlp_eda()

Versions
Pandas: 1.2.1
Numpy: 1.20.2


colors = {"coral": "#FC766AFF", "pacific coast":"#5B84B1FF" , "black": "#101820FF", "orange": "#F2AA4CFF"}

colors2 = {"pacific coast":"#5B84B1", "coral":"#FC766A" , "red": "#DC5757", "blue": "#4547CA",
          "teal": "#8AF3CC"}


!ls ../data

OOH                      heading_counts_clean.csv merged_headings_df.csv
chromedriver             headings                 models
chromedriver2            lda_results              scraping_results
heading_counts.csv       merged_df.csv


paths = {'all_data': '../data/merged_df.csv', "headings": "../data/merged_headings_df.csv"}

# ingest
all_data = pd.read_csv(paths['all_data'], index_col = 0)
headings_df = pd.read_csv(paths['headings'], index_col = 0)

def clean(seq):
    ''' This is the preprocessing pipeline that I do with the Random Forest in mind '''
    # converting to lowercase
    seq = seq.lower()
    
    # removing \n
    to_remove = {'\n','analyst','scientist', 'science', 'machine', 'learning','scientists', "experience"}

    resultwords  = [word for word in re.split("\W+",seq) if word.lower() not in to_remove]
    resultwords.remove("") # removing empty strings
    
    # converting back to string
    seq = " ".join(resultwords)
    
    return seq

# applying transformation
all_data['Description'] = all_data['Description'].apply(clean)

# subsets
ds = all_data[all_data['Job Title'] == "data scientist"]
da = all_data[all_data['Job Title'] == "data analyst"]

# getting word counts
ds_words = nlp.to_wcdf(ds['Description']).sum()
da_words = nlp.to_wcdf(da['Description']).sum()


all_data


# unit testing
example = all_data['Description'].iloc[0]

to_remove = {'\n','analyst','scientist', 'science'}

resultwords  = [word for word in re.split("\W+",example) if word.lower() not in to_remove]
resultwords.remove("")

" ".join(resultwords)[:100]

'this position can be based remotely anywhere in the usa or based in tonawanda ny linde is a leading '


all_data.head()


def visualize_seq_lengths(seq, title = "Distribution of Word Lengths", color = None):
    '''
    Parameters:
    -----------
    seq: A Pandas Series or simply a list 
    containing sequences of tokens
    '''
    seq = seq.apply(lambda x: x.split())
    print(seq)
    seq_lengths = [len(line) for line in seq]
    plt.figure(figsize = (16, 5))
    sns.distplot(seq_lengths, color = color, 
        norm_hist = True)
    plt.xlabel("Sequence Length")
    plt.title(title)
    plt.show()


visualize_seq_lengths(all_data['Description'], "Distribution of Sequence Lengths", color = colors2['teal'])

0      [this, position, can, be, based, remotely, any...
1      [summary, imagine, what, you, could, do, here,...
2      [lead, analytics, and, measurement, efforts, f...
3      [about, gusto, gusto, is, a, modern, online, p...
4      [summary, at, apple, new, ideas, have, a, way,...
                             ...                        
355    [the, new, york, post, is, searching, for, a, ...
356    [the, metro, group, inc, is, an, established, ...
357    [it, s, fun, to, work, in, a, company, where, ...
358    [summary, the, business, intelligence, bi, sol...
359    [location, new, york, new, york, global, about...
Name: Description, Length: 1703, dtype: object

/Users/mtaruno/opt/anaconda3/envs/cannon/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning:

`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).


# looking at how many headings there are
headings_df['Heading Title'].value_counts()

responsibilities                     363
qualifications                       358
job description                      211
location                             190
preferred qualifications             166
                                    ... 
excellent analytical skills            1
ten years related work experience      1
internal jobcode 70127                 1
experience with gcp                    1
why ft partners                        1
Name: Heading Title, Length: 1636, dtype: int64


# We can look at this separately for job description and job requirements
heading_name = 'job description'
headings_df[headings_df['Heading Title']== heading_name]


headings_df['Person/Job/Org/None'].value_counts()

Job       2383
Person    2293
Org        893
None       660
?           52
Name: Person/Job/Org/None, dtype: int64


nlp.visualize_seq_lengths(ds['Description'], "Seq Length Dist for DS", color = colors2['red'])
nlp.visualize_seq_lengths(da['Description'], "Seq Length Dist for DA", color = colors2['blue'])

/Users/mtaruno/opt/anaconda3/envs/cannon/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

/Users/mtaruno/opt/anaconda3/envs/cannon/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)


# import pymc3 as pm


# with pm.Model() as model:
# group1_mean = pm.Normal('group1_mean', mean_prior_mean, sd=mean_prior_std)
#     group2_mean = pm.Normal('group2_mean', mean_prior_mean, sd=mean_prior_std)


headings_df.head()


# Null values check
non_null_headings = headings_df[~headings_df['Heading Text'].isna()]

non_null_headings.isna().sum()

ID                       0
Heading Text             0
Heading Title            0
Frequency                0
Person/Job/Org/None    774
dtype: int64


non_null_headings.head()


non_null_headings.shape

(6373, 5)


heading_text_counts = nlp.to_wcdf(non_null_headings['Heading Text']).sum().sort_values(ascending=
            False).iloc[:30]

bar(x = heading_text_counts.index, y = heading_text_counts.values, 
    title = "Visualizing Top 30 Most Frequent Words in Corpus")


heading_text_counts

experience     1488
business       1359
science         997
team            958
work            896
degree          782
analytics       715
years           696
analysis        544
computer        544
learning        540
related         528
product         497
new             476
engineering     475
field           458
research        452
management      436
analyst         436
health          431
technology      419
machine         418
support         398
solutions       396
skills          395
people          393
world           390
statistics      388
across          377
including       371
dtype: int64


# Looking at the distribution of words
nlp.visualize_counts(non_null_headings['Heading Text'])

/Users/mtaruno/opt/anaconda3/envs/cannon/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(


nlp.visualize_seq_lengths(non_null_headings['Heading Text'])

/Users/mtaruno/opt/anaconda3/envs/cannon/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)


%run ../bar.py

Great! Enabled axis to bar function


top_ds = set(ds_words.sort_values(ascending=False)[:50].index)
top_da = set(da_words.sort_values(ascending=False)[:50].index)


# these are words that appear in both the DS and DA
intersection = top_ds.intersection(top_da)
intersection

{'ability',
 'across',
 'analysis',
 'analytics',
 'business',
 'company',
 'degree',
 'develop',
 'help',
 'including',
 'information',
 'insights',
 'knowledge',
 'new',
 'opportunity',
 'people',
 'product',
 'research',
 'role',
 'skills',
 'solutions',
 'sql',
 'status',
 'strong',
 'support',
 'team',
 'teams',
 'technical',
 'tools',
 'us',
 'using',
 'work',
 'working',
 'years'}


# words that are in ds but not in da
ds_not_da = top_ds - top_da
ds_not_da

{'build',
 'building',
 'design',
 'development',
 'engineering',
 'gender',
 'modeling',
 'models',
 'problems',
 'products',
 'python',
 'statistical',
 'statistics',
 'techniques',
 'use',
 'world'}


# words that are in da but not in ds
da_not_ds = top_da - top_ds
da_not_ds

{'analytical',
 'environment',
 'health',
 'job',
 'management',
 'position',
 'projects',
 'provide',
 'quality',
 'related',
 'reporting',
 'reports',
 'required',
 'requirements',
 'systems',
 'time'}


import plotly.express as px

# creating stacked bar chart of top intersection terms

ds_wc = nlp.to_wcdf(ds['Description']).sum()
da_wc = nlp.to_wcdf(da['Description']).sum()

da_wc_mask = [i in intersection for i in da_wc.index]
ds_wc_mask = [i in intersection for i in ds_wc.index]


ds_wc_final = ds_wc[ds_wc_mask].reset_index()
ds_wc_final["title"] = ["DS"] * ds_wc_final.shape[0]
da_wc_final = da_wc[da_wc_mask].reset_index()
da_wc_final["title"] = ["DA"] * da_wc_final.shape[0]

combined = pd.concat([ds_wc_final, da_wc_final], axis = 0)
combined = combined.rename(columns = {"index": "word", 0: "counts"}).sort_values("counts", ascending = False)

px.bar(combined, x = "word", y = "counts", color = "title", title = "Comparing Tokens That Appear in Both DS and DA",
      barmode = "group")


# creating a word cloud for top ds words
ds_word_counts = ds_words.reset_index().rename(columns = {"index": "token", 0:"counts"})

# creating a word cloud for top ds words
da_word_counts = da_words.reset_index().rename(columns = {"index": "token", 0:"counts"})


top_ds_words = ds_word_counts[ds_word_counts['token'].apply(lambda x: x in ds_not_da)].sort_values("counts", ascending=False)
top_da_words = da_word_counts[da_word_counts['token'].apply(lambda x: x in da_not_ds)].sort_values("counts", ascending=False)


import random


da_text = []
ds_text = []
both_text = []

for i, j in zip(top_da_words['token'], top_da_words['counts']):
    da_text += [i] * j

for i, j in zip(top_ds_words['token'], top_ds_words['counts']):
    ds_text += [i] * j

# Needs shuffling in order to work
random.shuffle(da_text)
random.shuffle(ds_text)

# plot counts
da_counts = nlp.to_wcdf(da_text).sum().sort_values(ascending=False)
bar(da_counts.index, da_counts.values, title = "DA not in DS", color = colors2["blue"])
ds_counts = nlp.to_wcdf(ds_text).sum().sort_values(ascending=False)
bar(ds_counts.index, ds_counts.values, title = "DS not in DA", color = colors2["red"])

# convert list to string format
da_text = " ".join(da_text)
ds_text = " ".join(ds_text)


from wordcloud import WordCloud


def plotcloud(wordcloud):
    plt.figure(figsize = (8,8))
    plt.imshow(wordcloud, interpolation="bilinear")
    plt.axis("off")
    plt.show()
    
da_wc = WordCloud(max_font_size=50, max_words=100, background_color=colors2["blue"], 
                  prefer_horizontal = 0,
                  stopwords = ['experience']).generate(da_text)
ds_wc = WordCloud(max_font_size=50, max_words=100, background_color=colors2['red'],
                  prefer_horizontal = 0,
                 stopwords = ['experience']).generate(ds_text)
print("DA Cloud \n")
plotcloud(da_wc)
print("DS Cloud \n")
plotcloud(ds_wc)

DA Cloud

DS Cloud


%run ../bar.py

Great! Enabled axis to bar function


title = all_data['Job Title']
x = title.value_counts().index
y = title.value_counts().values
bar(x,y, title = "Job Title Distribution")


import datetime


# Let's stick to data science vs data analyst
data = all_data[(all_data['Job Title'] == "data scientist")|(all_data['Job Title'] == "data analyst")]

# Getting X feature vector
feature_names = nlp.to_wcdf(data['Description']).columns.tolist()
text_data = nlp.to_wcdf(data['Description']).to_numpy()


from sklearn.model_selection import train_test_split
# Focusing on the inbound text
X = text_data
y = data['Job Title']
# Splitting, with stratify param for class balance
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size = 0.33, 
                                                    random_state = 25, stratify = y)


headings_df['Heading Title'].value_counts()

responsibilities                     363
qualifications                       358
job description                      211
location                             190
preferred qualifications             166
                                    ... 
excellent analytical skills            1
ten years related work experience      1
internal jobcode 70127                 1
experience with gcp                    1
why ft partners                        1
Name: Heading Title, Length: 1636, dtype: int64


%time

# Initializing my pipeline
estimators = [('model', RandomForestClassifier())]

pipe = Pipeline(estimators)

# These are the hyperparamaters and models I want to tune
param_grid = [{'model': [RandomForestClassifier()], 
               'model__n_estimators': [*range(50,501,100)]}, 
              ]

# 5 fold cross validation
grid = GridSearchCV(pipe, param_grid, cv = 5, verbose = 3)
fitted_grid = grid.fit(X_train, y_train)

CPU times: user 3 µs, sys: 3 µs, total: 6 µs
Wall time: 12.2 µs
Fitting 5 folds for each of 5 candidates, totalling 25 fits
[CV] model=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False), model__n_estimators=50

[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.

[CV]  model=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False), model__n_estimators=50, score=0.930, total=   0.3s
[CV] model=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False), model__n_estimators=50 
[CV]  model=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False), model__n_estimators=50, score=0.904, total=   0.1s
[CV] model=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False), model__n_estimators=50

[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.3s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.4s remaining:    0.0s

[CV]  model=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False), model__n_estimators=50, score=0.852, total=   0.1s
[CV] model=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False), model__n_estimators=50 
[CV]  model=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False), model__n_estimators=50, score=0.930, total=   0.1s
[CV] model=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False), model__n_estimators=50 
[CV]  model=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False), model__n_estimators=50, score=0.807, total=   0.1s
[CV] model=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False), model__n_estimators=150 
[CV]  model=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False), model__n_estimators=150, score=0.939, total=   0.6s
[CV] model=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False), model__n_estimators=150 
[CV]  model=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False), model__n_estimators=150, score=0.878, total=   0.6s
[CV] model=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False), model__n_estimators=150 
[CV]  model=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False), model__n_estimators=150, score=0.878, total=   0.5s
[CV] model=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False), model__n_estimators=150 
[CV]  model=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False), model__n_estimators=150, score=0.922, total=   0.4s
[CV] model=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False), model__n_estimators=150 
[CV]  model=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False), model__n_estimators=150, score=0.851, total=   0.5s
[CV] model=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False), model__n_estimators=250 
[CV]  model=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False), model__n_estimators=250, score=0.957, total=   1.0s
[CV] model=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False), model__n_estimators=250 
[CV]  model=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False), model__n_estimators=250, score=0.904, total=   0.8s
[CV] model=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False), model__n_estimators=250 
[CV]  model=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False), model__n_estimators=250, score=0.861, total=   0.6s
[CV] model=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False), model__n_estimators=250 
[CV]  model=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False), model__n_estimators=250, score=0.896, total=   0.6s
[CV] model=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False), model__n_estimators=250 
[CV]  model=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False), model__n_estimators=250, score=0.868, total=   0.8s
[CV] model=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False), model__n_estimators=350 
[CV]  model=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False), model__n_estimators=350, score=0.957, total=   0.9s
[CV] model=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False), model__n_estimators=350 
[CV]  model=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False), model__n_estimators=350, score=0.896, total=   0.8s
[CV] model=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False), model__n_estimators=350 
[CV]  model=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False), model__n_estimators=350, score=0.852, total=   0.8s
[CV] model=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False), model__n_estimators=350 
[CV]  model=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False), model__n_estimators=350, score=0.904, total=   0.8s
[CV] model=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False), model__n_estimators=350 
[CV]  model=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False), model__n_estimators=350, score=0.868, total=   0.9s
[CV] model=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False), model__n_estimators=450 
[CV]  model=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False), model__n_estimators=450, score=0.922, total=   1.1s
[CV] model=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False), model__n_estimators=450 
[CV]  model=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False), model__n_estimators=450, score=0.904, total=   1.1s
[CV] model=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False), model__n_estimators=450 
[CV]  model=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False), model__n_estimators=450, score=0.870, total=   1.3s
[CV] model=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False), model__n_estimators=450 
[CV]  model=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False), model__n_estimators=450, score=0.904, total=   1.1s
[CV] model=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False), model__n_estimators=450 
[CV]  model=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False), model__n_estimators=450, score=0.860, total=   1.1s

[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:   17.1s finished


from sklearn.externals import joblib

# getting string of now
now = str(datetime.datetime.now())[:16]

# Saving it because it took some time to run
joblib.dump(fitted_grid, f'../data/models/{now}.pkl', compress=1)

['../data/models/2021-04-28 21:37.pkl']


# Here we have a dataframe of all the fits under the different hyperparamaters
results = pd.DataFrame(fitted_grid.cv_results_).sort_values('mean_test_score', ascending = False)
results


bar(x = [*range(len(results))], y = results['mean_test_score'], 
    title = 'Accuracies are Very Similar')


rf = grid.best_estimator_ # getting best param rf
rf.fit(X_train, y_train) # fitting this to get some results

Pipeline(memory=None,
         steps=[('model',
                 RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,
                                        class_weight=None, criterion='gini',
                                        max_depth=None, max_features='auto',
                                        max_leaf_nodes=None, max_samples=None,
                                        min_impurity_decrease=0.0,
                                        min_impurity_split=None,
                                        min_samples_leaf=1, min_samples_split=2,
                                        min_weight_fraction_leaf=0.0,
                                        n_estimators=250, n_jobs=None,
                                        oob_score=False, random_state=None,
                                        verbose=0, warm_start=False))],
         verbose=False)


print(plt.style.available)

['Solarize_Light2', '_classic_test_patch', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn', 'seaborn-bright', 'seaborn-colorblind', 'seaborn-dark', 'seaborn-dark-palette', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted', 'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk', 'seaborn-ticks', 'seaborn-white', 'seaborn-whitegrid', 'tableau-colorblind10']


# Some more evaluation metrics with F1
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.metrics import classification_report
from sklearn.metrics import f1_score
from sklearn.metrics import confusion_matrix

plt.style.use('seaborn')

def evaluate(model):
    
    # Scoring the model
    print(f'Score on train: {model.score(X_train, y_train)}')
    print(f'Score on test: {model.score(X_test, y_test)}')

    y_pred = model.predict(X_test)
    
    print('\nConfusion Matrix:')
    cm = confusion_matrix(y_test, y_pred)
    plt.figure(figsize = (10,8))
    ConfusionMatrixDisplay(cm, display_labels = y.unique()).plot(xticks_rotation = 'vertical', 
                        values_format = 'd')
    plt.show()
    print('\nClassification Report:')
    print(classification_report(y_test, y_pred))
    print('\nF1 Score:')
    print(f1_score(y_test, y_pred, average = None))


evaluate(rf)

Score on train: 1.0
Score on test: 0.9190140845070423

Confusion Matrix:

<Figure size 720x576 with 0 Axes>

Classification Report:
                precision    recall  f1-score   support

  data analyst       0.89      0.95      0.92       133
data scientist       0.95      0.89      0.92       151

      accuracy                           0.92       284
     macro avg       0.92      0.92      0.92       284
  weighted avg       0.92      0.92      0.92       284


F1 Score:
[0.91636364 0.92150171]


importances = pd.DataFrame({"Feature": feature_names, "Feature Importance": rf[0].feature_importances_})

importances.sort_values("Feature Importance", ascending = False, inplace = True)

importances.head(30)


bar(x = importances.head(30).Feature, y = importances.head(30)['Feature Importance'],
   title = "Which Tokens Are Important In Distinguishing Data Scientist vs Data Analyst Roles?")

	ID	Heading Text	Heading Title	Frequency	Person/Job/Org/None
1217	15	At Rockwell Automation, we offer transformativ...	job description	189	Job
1218	26	Resideo is reinventing how we look at the home...	job description	189	Job
1219	31	We’re hiring a Growth Analytics Data Scientist...	job description	189	Job
1220	40	Square provides tools and services that enable...	job description	189	Job
1221	61	NaN	job description	189	Job
...	...	...	...	...	...
1423	1680	NaN	job description	189	Job
1424	1682	The Mayor's Office of Contract Services (MOCS)...	job description	189	Job
1425	1697	The New York City Department of Health and Men...	job description	189	Job
1426	1700	NaN	job description	189	Job
1427	1702	Our clients are making investments in smart gr...	job description	189	Job

	mean_fit_time	std_fit_time	mean_score_time	std_score_time	param_model	param_model__n_estimators	params	split0_test_score	split1_test_score	split2_test_score	split3_test_score	split4_test_score	mean_test_score	std_test_score	rank_test_score
2	0.755047	0.141881	0.015445	0.003750	RandomForestClassifier(bootstrap=True, ccp_alp...	250	{'model': RandomForestClassifier(bootstrap=Tru...	0.956522	0.904348	0.860870	0.895652	0.868421	0.897162	0.033825	1
3	0.816151	0.036132	0.017999	0.000464	RandomForestClassifier(bootstrap=True, ccp_alp...	350	{'model': RandomForestClassifier(bootstrap=Tru...	0.956522	0.895652	0.852174	0.904348	0.868421	0.895423	0.035812	2
1	0.496576	0.089171	0.013503	0.003749	RandomForestClassifier(bootstrap=True, ccp_alp...	150	{'model': RandomForestClassifier(bootstrap=Tru...	0.939130	0.878261	0.878261	0.921739	0.850877	0.893654	0.032127	3
4	1.115917	0.082877	0.023660	0.000932	RandomForestClassifier(bootstrap=True, ccp_alp...	450	{'model': RandomForestClassifier(bootstrap=Tru...	0.921739	0.904348	0.869565	0.904348	0.859649	0.891930	0.023406	4
0	0.150759	0.055983	0.004534	0.001005	RandomForestClassifier(bootstrap=True, ccp_alp...	50	{'model': RandomForestClassifier(bootstrap=Tru...	0.930435	0.904348	0.852174	0.930435	0.807018	0.884882	0.048294	5

	Feature	Feature Importance
2643	machine	0.026397
2525	learning	0.020729
3684	reporting	0.019383
3868	science	0.018598
1650	excel	0.012896
3502	python	0.011716
2825	models	0.010913
3872	scientists	0.009661
3202	phd	0.008929
233	algorithms	0.007832
3685	reports	0.007651
2817	ml	0.006797
457	bachelor	0.006444
603	build	0.005169
3319	predictive	0.004793
4089	spark	0.004693
4167	statistics	0.004665
326	applied	0.004412
1141	dashboards	0.004071
2426	java	0.003970
4358	tensorflow	0.003716
1692	experimentation	0.003589
3850	scala	0.003544
3853	scale	0.003417
4345	techniques	0.003372
2666	management	0.003355
1543	engineering	0.003188
4125	sql	0.003067
1170	deep	0.002972
2785	microsoft	0.002959

Table of Contents:¶

Cleaning List¶

Todo¶

EDA¶

Headings Data ¶

Set Analysis¶

Random Forest Feature Importances¶

	Description	Location	Job Title
0	this position can be based remotely anywhere i...	new york	data scientist
1	summary imagine what you could do here at appl...	texas	data scientist
2	lead analytics and measurement efforts for str...	other	data scientist
3	about gusto gusto is a modern online people pl...	other	data scientist
4	summary at apple new ideas have a way of becom...	texas	data scientist
...	...	...	...
355	the new york post is searching for a web to tu...	new york	other
356	the metro group inc is an established successf...	new york	business anlayst
357	it s fun to work in a company where people tru...	new york	data analyst
358	summary the business intelligence bi solutions...	new york	other
359	location new york new york global about indigo...	new york	other

	ID	Heading Text	Heading Title	Frequency	Person/Job/Org/None
0	0	This is a unique opportunity to collaborate wi...	position summary	56	Job
1	43	We are looking for a full time Data Scientist ...	position summary	56	Job
2	157	The Senior Data Analyst is responsible for ana...	position summary	56	Job
3	176	In this role you'll be working on a team desig...	position summary	56	Job
4	183	The Senior Data Analyst is responsible for ana...	position summary	56	Job